Beijing Key Laboratory of Drug Target Research and Drug Screening

Beijing, China

Beijing Key Laboratory of Drug Target Research and Drug Screening

Beijing, China

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Lian W.,Peking Union Medical College | Fang J.,Peking Union Medical College | Li C.,Peking Union Medical College | Pang X.,Peking Union Medical College | And 6 more authors.
Molecular Diversity | Year: 2016

Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with (Formula presented.) values in the range of 12.9–185.0 nM, and against H3N2 NA with (Formula presented.) values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with (Formula presented.) values ranging 39.5–63.8  (Formula presented.) M against H1N1 NA and 44.5–114.1  (Formula presented.) M against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays. © 2015, Springer International Publishing Switzerland.

Gao L.,Peking Union Medical College | Gao L.,Modern Medicine | Li C.,Peking Union Medical College | Yang R.-Y.,Peking Union Medical College | And 8 more authors.
Pharmacology Biochemistry and Behavior | Year: 2015

Baicalein, a flavonoid from Scutellaria baicalensis Georgi, has been shown to possess neuroprotective properties. The purpose of this study was to explore the effects of baicalein on motor behavioral deficits and gene expression in N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mice model of Parkinson's disease (PD). The behavioral results showed that baicalein significantly improves the abnormal behaviors in MPTP-induced mice model of PD, as manifested by shortening the total time for climbing down the pole, prolonging the latent periods of rotarod, and increasing the vertical movements. Using cDNA microarray and subsequent bioinformatic analyses, it was found that baicalein significantly promotes the biological processes including neurogenesis, neuroblast proliferation, neurotrophin signaling pathway, walking and locomotor behaviors, and inhibits dopamine metabolic process through regulation of gene expressions. Based on analysis of gene co-expression networks, the results indicated that the regulation of genes such as LIMK1, SNCA and GLRA1 by baicalein might play central roles in the network. Our results provide experimental evidence for the potential use of baicalein in the treatment of PD, and revealed gene expression profiles, biological processes and pathways influenced by baicalein in MPTP-treated mice. © 2015 Elsevier Inc. All rights reserved.

Fang J.,Peking Union Medical College | Yang R.,Peking Union Medical College | Gao L.,Peking Union Medical College | Zhou D.,Peking Union Medical College | And 7 more authors.
Journal of Chemical Information and Modeling | Year: 2013

Butyrylcholinesterase (BuChE, EC is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 μM, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds. © 2013 American Chemical Society.

Li C.,Peking Union Medical College | Fang J.-S.,Peking Union Medical College | Lian W.-W.,Peking Union Medical College | Pang X.-C.,Peking Union Medical College | And 6 more authors.
Chemical Biology and Drug Design | Year: 2015

The anti-influenza virus activities of 50 resveratrol (RV: 3, 5, 4′-trihydroxy-trans-stilbene) derivatives were evaluated using a neuraminidase (NA) activity assay. The results showed that 35 compounds exerted an inhibitory effect on the NA activity of the influenza virus strain A/PR/8/34 (H1N1) with 50% inhibitory concentration (IC50) values ranging from 3.56 to 186.1 μm. Next, the 35 RV derivatives were used to develop 3D quantitative structure-activity relationship (3D QSAR) models for understanding the chemical-biological interactions governing their activities against NA. The comparative molecular field analysis (CoMFA r2 = 0.973, q2 = 0.620, qtest2 = 0.661) and the comparative molecular similarity indices analysis (CoMSIA r2 = 0.956, q2 = 0.610, qtest2 = 0.531) were applied. Afterward, molecular docking was performed to study the molecular interactions between the RV derivatives and NA. Finally, a cytopathic effect (CPE) reduction assay was used to evaluate the antiviral effects of the RV derivatives in vitro. Time-of-addition studies demonstrated that the RV derivatives might have a direct effect on viral particle infectivity. Our results indicate that the RV derivatives are potentially useful antiviral compounds for new drug design and development for influenza treatment. © 2014 John Wiley & Sons A/S.

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